In this paper a Bayesian approach to unit root testing for panel data models is proposed based on the comparison of stationary autoregressive models with and without individual deterministic trends, with their counterpart models with a unit autoregressive root. This is done under cross-sectional dependence among the units of the panel. Simulation experiments are conducted with the aim to assess...

Forecasting with Spatial Panel Data This paper compares various forecasts using panel data with spatial error correlation. The true data generating process is assumed to be a simple error component regression model with spatial remainder disturbances of the autoregressive or moving average type. The best linear unbiased predictor is compared with other forecasts ignoring spatial correlation, or...

Random Coefficient Panel Data Models This paper provides a review of linear panel data models with slope heterogeneity, introduces various types of random coefficients models and suggest a common framework for dealing with them. It considers the fundamental issues of statistical inference of a random coefficients formulation using both the sampling and Bayesian approaches. The paper also provid...

This paper tests Wagner’s law of increasing state activity using panels of Chinese provinces. The paper’s main methodological contribution is in that we employ for the first time in the literature on Wagner’s law a panel unit root, panel cointegration and Granger Causality testing approach. Overall, we find mixed evidence in support of Wagner’s law for China’s central and western provinces, but...

This paper investigates whether the Feldstein and Horioka (1980) argument on domestic saving-investment relationship is supported by the data of the countries in the Middle East and North Africa (MENA) region when the financial development levels and exchange rate regimes are taken into account. To this end, we employ both the ARDL bounds cointegration test and panel mean group procedures. The ...

Charitable giving data from a new Panel Study of Income Dynamics (PSID) philanthropy module are introduced. Relative distribution methods are used to compare the data to Internal Revenue Service (IRS) charitable deductions. Results indicate the new data are high quality. JEL Classification: C80, D64, H40.